It is generally desirable to improve the perceptual image quality of interpolated images produced by a motion compensation system. A typical motion compensation system consists of two major building blocks. The first block is the motion estimation process that produces a motion vector field, which describes the local displacement of image parts estimated from two images at distinct but usually adjacent points in time. The estimated motion vector information is then used by the interpolation process to displace those image parts in motion by a fractional amount τ that represents the intermediate temporal position of the image to be interpolated. FIG. 1 depicts this situation for a one-dimensional image containing a still background object BG and a moving foreground object FG. The displacement of the border between BG and FG from time n to time n+1 is given by the vector v. To properly interpolate an image at time n+τ, the vector v has to be scaled by factor τ to find the interpolated position of the border between BG and FG. The line a denotes the access position in the previous and next images at time index n and n+1 respectively, where the image parts at those positions are used in a filtering process to produce the interpolated image part at point p.
The interpolation process will however not reproduce the proper border between objects BG and FG if the estimated motion vector v, for some reason, does not reflect the actual motion of object FG. This situation is visualized in FIG. 2, where the motion estimation results in the wrong vector v′ instead of the correct vector v. As a consequence, the border between the two objects includes now a transition area between the access lines b and c, and the interpolation process will be based on a mix of foreground and background areas. In general, this constellation will result in the border area not being properly constructed and therefore decrease the perceptual quality of the interpolation result.
The reasons for the failure of the motion estimation process can be manifold. They are related to the principle of motion estimation itself or due to implementation related constraints. Prominent causes for failing motion estimation are:                When a foreground object depicted in an image sequence moves over a background object, then parts of the background occluded by the foreground in the previous image will be exposed in the next image. Contrary, parts of the background image exposed in the previous images might be occluded by the foreground object when it moves over those parts. Most motion estimation processes still try to estimate a motion vector for those occlusion and exposure areas. This is however prone to error, because the information used in the matching process is only present in either the previous or the next image.        If the matching process has determined several candidates for the displacement vector, all of them resulting in equally good matches with respect to the matching criterion, the finally assigned vector might be a random selection from the set of candidates. This situation arises in case of uniform image areas or areas containing a repetitive pattern or texture.        
While the problems incurred by an imperfect motion estimation process cannot be cured for the reasons given above, it is an objective of the proposed method to decrease the visibility of those effects in the interpolation result, and thus improve the perceptual quality of the motion compensation. It is another objective of the proposed method to achieve this result with an efficient approach that is suitable for implementation in an integrated circuit. In other words, it is an objective to provide a signal processing means that can decrease the visibility of effects incurred by imperfect motion estimation in the interpolation result, and thus improve the perceptual quality of the motion compensation, wherein the resources in terms of needed computing time and memory is kept at a minimum.